Overview

Brought to you by YData

Dataset statistics

Number of variables38
Number of observations4142
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory312.0 B

Variable types

Categorical24
Unsupported2
Numeric12

Alerts

IsAwareness has constant value "1" Constant
Trial has constant value "1" Constant
BUMO PRE is highly overall correlated with Churn and 3 other fieldsHigh correlation
Brand Likability is highly overall correlated with DailyHigh correlation
Churn is highly overall correlated with BUMO PRE and 3 other fieldsHigh correlation
Daily is highly overall correlated with Brand Likability and 1 other fieldsHigh correlation
Most Favourite is highly overall correlated with BUMO PRE and 1 other fieldsHigh correlation
NPS#P3M is highly overall correlated with NPS#P3M#GroupHigh correlation
NPS#P3M#Group is highly overall correlated with NPS#P3MHigh correlation
PPA is highly overall correlated with BUMO PRE and 5 other fieldsHigh correlation
Spend is highly overall correlated with PPA and 3 other fieldsHigh correlation
TOM is highly overall correlated with BUMO PRE and 5 other fieldsHigh correlation
Visit is highly overall correlated with PPA and 2 other fieldsHigh correlation
Weekly is highly overall correlated with Churn and 5 other fieldsHigh correlation
BUMO is highly imbalanced (82.9%) Imbalance
DayPart_Unknown is highly imbalanced (99.4%) Imbalance
DayofWeek_Unknown is highly imbalanced (96.2%) Imbalance
Weekends is highly imbalanced (65.9%) Imbalance
Socializing is highly imbalanced (68.8%) Imbalance
Studying is highly imbalanced (77.8%) Imbalance
NeedState_Other is highly imbalanced (96.2%) Imbalance
Comprehension is an unsupported type, check if it needs cleaning or further analysis Unsupported
SegmentationFull is an unsupported type, check if it needs cleaning or further analysis Unsupported
Visit has 1365 (33.0%) zeros Zeros
Spend has 1365 (33.0%) zeros Zeros
11 AM - before 2 PM has 3451 (83.3%) zeros Zeros
2 PM - before 5 PM has 3306 (79.8%) zeros Zeros
5 PM - before 9 PM has 1358 (32.8%) zeros Zeros
9 AM - before 11 AM has 3206 (77.4%) zeros Zeros
9 PM or later has 3690 (89.1%) zeros Zeros
Before 9 AM has 2741 (66.2%) zeros Zeros
PPA has 1365 (33.0%) zeros Zeros

Reproduction

Analysis started2025-06-21 05:28:18.053526
Analysis finished2025-06-21 05:28:41.270473
Duration23.22 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

IsAwareness
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
1
4142 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4142
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 4142
100.0%

Length

2025-06-21T05:28:41.371223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-21T05:28:41.430896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 4142
100.0%

Most occurring characters

ValueCountFrequency (%)
1 4142
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 4142
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 4142
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 4142
100.0%

Trial
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
1
4142 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4142
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 4142
100.0%

Length

2025-06-21T05:28:41.498963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-21T05:28:41.557696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 4142
100.0%

Most occurring characters

ValueCountFrequency (%)
1 4142
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 4142
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 4142
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 4142
100.0%

Brand Likability
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
0
2993 
1
1149 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4142
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 2993
72.3%
1 1149
 
27.7%

Length

2025-06-21T05:28:41.622708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-21T05:28:41.686250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2993
72.3%
1 1149
 
27.7%

Most occurring characters

ValueCountFrequency (%)
0 2993
72.3%
1 1149
 
27.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2993
72.3%
1 1149
 
27.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2993
72.3%
1 1149
 
27.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2993
72.3%
1 1149
 
27.7%

Weekly
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
0
2473 
1
1669 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4142
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2473
59.7%
1 1669
40.3%

Length

2025-06-21T05:28:41.767147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-21T05:28:41.829865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2473
59.7%
1 1669
40.3%

Most occurring characters

ValueCountFrequency (%)
0 2473
59.7%
1 1669
40.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2473
59.7%
1 1669
40.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2473
59.7%
1 1669
40.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2473
59.7%
1 1669
40.3%

Daily
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
0
3310 
1
832 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4142
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3310
79.9%
1 832
 
20.1%

Length

2025-06-21T05:28:41.919002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-21T05:28:41.980906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 3310
79.9%
1 832
 
20.1%

Most occurring characters

ValueCountFrequency (%)
0 3310
79.9%
1 832
 
20.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3310
79.9%
1 832
 
20.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3310
79.9%
1 832
 
20.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3310
79.9%
1 832
 
20.1%

Comprehension
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size64.7 KiB

Visit
Real number (ℝ)

High correlation  Zeros 

Distinct28
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0108643
Minimum0
Maximum40
Zeros1365
Zeros (%)33.0%
Negative0
Negative (%)0.0%
Memory size64.7 KiB
2025-06-21T05:28:42.068630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile10
Maximum40
Range40
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.154831
Coefficient of variation (CV)1.3799463
Kurtosis12.945293
Mean3.0108643
Median Absolute Deviation (MAD)2
Skewness2.9763701
Sum12471
Variance17.26262
MonotonicityNot monotonic
2025-06-21T05:28:42.194433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 1365
33.0%
2 628
15.2%
1 471
 
11.4%
4 455
 
11.0%
3 367
 
8.9%
5 197
 
4.8%
8 162
 
3.9%
6 138
 
3.3%
10 122
 
2.9%
7 53
 
1.3%
Other values (18) 184
 
4.4%
ValueCountFrequency (%)
0 1365
33.0%
1 471
 
11.4%
2 628
15.2%
3 367
 
8.9%
4 455
 
11.0%
5 197
 
4.8%
6 138
 
3.3%
7 53
 
1.3%
8 162
 
3.9%
9 24
 
0.6%
ValueCountFrequency (%)
40 1
 
< 0.1%
35 1
 
< 0.1%
30 14
 
0.3%
28 4
 
0.1%
27 2
 
< 0.1%
26 1
 
< 0.1%
25 4
 
0.1%
24 2
 
< 0.1%
20 41
1.0%
19 1
 
< 0.1%

Spend
Real number (ℝ)

High correlation  Zeros 

Distinct187
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean153.80146
Minimum0
Maximum2100
Zeros1365
Zeros (%)33.0%
Negative0
Negative (%)0.0%
Memory size64.7 KiB
2025-06-21T05:28:42.337757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median98
Q3200
95-th percentile549.5
Maximum2100
Range2100
Interquartile range (IQR)200

Descriptive statistics

Standard deviation216.17547
Coefficient of variation (CV)1.4055489
Kurtosis12.283241
Mean153.80146
Median Absolute Deviation (MAD)98
Skewness2.9072207
Sum637045.67
Variance46731.836
MonotonicityNot monotonic
2025-06-21T05:28:42.471090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1365
33.0%
100 190
 
4.6%
200 170
 
4.1%
120 136
 
3.3%
50 108
 
2.6%
300 101
 
2.4%
150 98
 
2.4%
60 86
 
2.1%
80 79
 
1.9%
240 75
 
1.8%
Other values (177) 1734
41.9%
ValueCountFrequency (%)
0 1365
33.0%
14 1
 
< 0.1%
15 1
 
< 0.1%
28 1
 
< 0.1%
29 23
 
0.6%
30 25
 
0.6%
35 21
 
0.5%
36 1
 
< 0.1%
39 30
 
0.7%
40 58
 
1.4%
ValueCountFrequency (%)
2100 1
 
< 0.1%
2000 1
 
< 0.1%
1770 1
 
< 0.1%
1750 1
 
< 0.1%
1600 2
< 0.1%
1534 1
 
< 0.1%
1500 4
0.1%
1400 4
0.1%
1372 1
 
< 0.1%
1350 1
 
< 0.1%

SegmentationFull
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size64.7 KiB

NPS#P3M
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4355384
Minimum-1
Maximum10
Zeros0
Zeros (%)0.0%
Negative15
Negative (%)0.4%
Memory size64.7 KiB
2025-06-21T05:28:42.574082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile6
Q18
median9
Q39
95-th percentile10
Maximum10
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.292735
Coefficient of variation (CV)0.15324866
Kurtosis10.091869
Mean8.4355384
Median Absolute Deviation (MAD)1
Skewness-1.9763808
Sum34940
Variance1.6711637
MonotonicityNot monotonic
2025-06-21T05:28:42.656756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
8 1340
32.4%
9 1336
32.3%
10 797
19.2%
7 438
 
10.6%
6 117
 
2.8%
5 83
 
2.0%
-1 15
 
0.4%
4 12
 
0.3%
2 2
 
< 0.1%
3 2
 
< 0.1%
ValueCountFrequency (%)
-1 15
 
0.4%
2 2
 
< 0.1%
3 2
 
< 0.1%
4 12
 
0.3%
5 83
 
2.0%
6 117
 
2.8%
7 438
 
10.6%
8 1340
32.4%
9 1336
32.3%
10 797
19.2%
ValueCountFrequency (%)
10 797
19.2%
9 1336
32.3%
8 1340
32.4%
7 438
 
10.6%
6 117
 
2.8%
5 83
 
2.0%
4 12
 
0.3%
3 2
 
< 0.1%
2 2
 
< 0.1%
-1 15
 
0.4%

NPS#P3M#Group
Categorical

High correlation 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
Promoter
2133 
Passive
1778 
Detractor
216 
Unknown
 
15

Length

Max length9
Median length8
Mean length7.6192661
Min length7

Characters and Unicode

Total characters31559
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPassive
2nd rowPassive
3rd rowPassive
4th rowPassive
5th rowPassive

Common Values

ValueCountFrequency (%)
Promoter 2133
51.5%
Passive 1778
42.9%
Detractor 216
 
5.2%
Unknown 15
 
0.4%

Length

2025-06-21T05:28:42.763476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-21T05:28:42.840900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
promoter 2133
51.5%
passive 1778
42.9%
detractor 216
 
5.2%
unknown 15
 
0.4%

Most occurring characters

ValueCountFrequency (%)
r 4698
14.9%
o 4497
14.2%
e 4127
13.1%
P 3911
12.4%
s 3556
11.3%
t 2565
8.1%
m 2133
6.8%
a 1994
6.3%
i 1778
 
5.6%
v 1778
 
5.6%
Other values (6) 522
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31559
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 4698
14.9%
o 4497
14.2%
e 4127
13.1%
P 3911
12.4%
s 3556
11.3%
t 2565
8.1%
m 2133
6.8%
a 1994
6.3%
i 1778
 
5.6%
v 1778
 
5.6%
Other values (6) 522
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31559
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 4698
14.9%
o 4497
14.2%
e 4127
13.1%
P 3911
12.4%
s 3556
11.3%
t 2565
8.1%
m 2133
6.8%
a 1994
6.3%
i 1778
 
5.6%
v 1778
 
5.6%
Other values (6) 522
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31559
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 4698
14.9%
o 4497
14.2%
e 4127
13.1%
P 3911
12.4%
s 3556
11.3%
t 2565
8.1%
m 2133
6.8%
a 1994
6.3%
i 1778
 
5.6%
v 1778
 
5.6%
Other values (6) 522
 
1.7%

Age
Real number (ℝ)

Distinct45
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.008691
Minimum16
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.7 KiB
2025-06-21T05:28:42.969092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile18
Q124
median30
Q337
95-th percentile48
Maximum60
Range44
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.0357588
Coefficient of variation (CV)0.29139439
Kurtosis0.22173823
Mean31.008691
Median Absolute Deviation (MAD)6
Skewness0.68603495
Sum128438
Variance81.644938
MonotonicityNot monotonic
2025-06-21T05:28:43.111410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
27 222
 
5.4%
29 203
 
4.9%
28 202
 
4.9%
26 176
 
4.2%
22 171
 
4.1%
30 162
 
3.9%
23 162
 
3.9%
35 161
 
3.9%
25 160
 
3.9%
34 160
 
3.9%
Other values (35) 2363
57.0%
ValueCountFrequency (%)
16 62
 
1.5%
17 76
1.8%
18 87
2.1%
19 110
2.7%
20 127
3.1%
21 134
3.2%
22 171
4.1%
23 162
3.9%
24 149
3.6%
25 160
3.9%
ValueCountFrequency (%)
60 13
0.3%
59 13
0.3%
58 7
 
0.2%
57 14
0.3%
56 15
0.4%
55 13
0.3%
54 11
0.3%
53 15
0.4%
52 19
0.5%
51 20
0.5%

MPI
Real number (ℝ)

Distinct32
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7937.9638
Minimum1499
Maximum112499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.7 KiB
2025-06-21T05:28:43.231915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1499
5-th percentile1499
Q15499
median7592
Q39924
95-th percentile17499
Maximum112499
Range111000
Interquartile range (IQR)4425

Descriptive statistics

Standard deviation4866.8055
Coefficient of variation (CV)0.61310503
Kurtosis61.270791
Mean7937.9638
Median Absolute Deviation (MAD)2093
Skewness4.2138774
Sum32879046
Variance23685796
MonotonicityNot monotonic
2025-06-21T05:28:43.355990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
6999 525
12.7%
5499 521
12.6%
8249 483
11.7%
10499 438
10.6%
1499 363
8.8%
8700 295
7.1%
3749 260
 
6.3%
13499 210
 
5.1%
9924 202
 
4.9%
2861 164
 
4.0%
Other values (22) 681
16.4%
ValueCountFrequency (%)
1499 363
8.8%
2861 164
 
4.0%
3749 260
6.3%
4420 10
 
0.2%
4454 99
 
2.4%
5499 521
12.6%
6819 12
 
0.3%
6999 525
12.7%
7020 107
 
2.6%
7121 6
 
0.1%
ValueCountFrequency (%)
112499 1
 
< 0.1%
59999 2
 
< 0.1%
37499 11
 
0.3%
27499 8
 
0.2%
22499 60
 
1.4%
18441 13
 
0.3%
17499 162
3.9%
16961 3
 
0.1%
13499 210
5.1%
12810 3
 
0.1%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
Female
2435 
Male
1707 

Length

Max length6
Median length6
Mean length5.1757605
Min length4

Characters and Unicode

Total characters21438
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowFemale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Female 2435
58.8%
Male 1707
41.2%

Length

2025-06-21T05:28:43.474617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-21T05:28:43.549011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
female 2435
58.8%
male 1707
41.2%

Most occurring characters

ValueCountFrequency (%)
e 6577
30.7%
a 4142
19.3%
l 4142
19.3%
F 2435
 
11.4%
m 2435
 
11.4%
M 1707
 
8.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21438
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 6577
30.7%
a 4142
19.3%
l 4142
19.3%
F 2435
 
11.4%
m 2435
 
11.4%
M 1707
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21438
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 6577
30.7%
a 4142
19.3%
l 4142
19.3%
F 2435
 
11.4%
m 2435
 
11.4%
M 1707
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21438
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 6577
30.7%
a 4142
19.3%
l 4142
19.3%
F 2435
 
11.4%
m 2435
 
11.4%
M 1707
 
8.0%

Occupation#group
Categorical

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
White Collar
1547 
None Working
984 
Blue Collar
779 
Self Employed - Small Business and Freelance
747 
Self Employed - Company Owner
 
60
Other values (2)
 
25

Length

Max length44
Median length12
Mean length17.84621
Min length6

Characters and Unicode

Total characters73919
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBlue Collar
2nd rowNone Working
3rd rowSelf Employed - Company Owner
4th rowBlue Collar
5th rowWhite Collar

Common Values

ValueCountFrequency (%)
White Collar 1547
37.3%
None Working 984
23.8%
Blue Collar 779
18.8%
Self Employed - Small Business and Freelance 747
18.0%
Self Employed - Company Owner 60
 
1.4%
Other Occupations 20
 
0.5%
Refuse 5
 
0.1%

Length

2025-06-21T05:28:43.656126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-21T05:28:43.771045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
collar 2326
19.1%
white 1547
12.7%
none 984
8.1%
working 984
8.1%
self 807
 
6.6%
807
 
6.6%
employed 807
 
6.6%
blue 779
 
6.4%
small 747
 
6.1%
business 747
 
6.1%
Other values (7) 1659
13.6%

Most occurring characters

ValueCountFrequency (%)
l 9286
12.6%
8052
 
10.9%
e 8002
 
10.8%
o 5181
 
7.0%
a 4647
 
6.3%
n 4349
 
5.9%
r 4137
 
5.6%
i 3298
 
4.5%
W 2531
 
3.4%
C 2386
 
3.2%
Other values (21) 22050
29.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73919
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 9286
12.6%
8052
 
10.9%
e 8002
 
10.8%
o 5181
 
7.0%
a 4647
 
6.3%
n 4349
 
5.9%
r 4137
 
5.6%
i 3298
 
4.5%
W 2531
 
3.4%
C 2386
 
3.2%
Other values (21) 22050
29.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73919
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 9286
12.6%
8052
 
10.9%
e 8002
 
10.8%
o 5181
 
7.0%
a 4647
 
6.3%
n 4349
 
5.9%
r 4137
 
5.6%
i 3298
 
4.5%
W 2531
 
3.4%
C 2386
 
3.2%
Other values (21) 22050
29.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73919
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 9286
12.6%
8052
 
10.9%
e 8002
 
10.8%
o 5181
 
7.0%
a 4647
 
6.3%
n 4349
 
5.9%
r 4137
 
5.6%
i 3298
 
4.5%
W 2531
 
3.4%
C 2386
 
3.2%
Other values (21) 22050
29.8%

TOM
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
0
2661 
1
1481 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4142
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2661
64.2%
1 1481
35.8%

Length

2025-06-21T05:28:43.895256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-21T05:28:43.976105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2661
64.2%
1 1481
35.8%

Most occurring characters

ValueCountFrequency (%)
0 2661
64.2%
1 1481
35.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2661
64.2%
1 1481
35.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2661
64.2%
1 1481
35.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2661
64.2%
1 1481
35.8%

BUMO
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
0
4037 
1
 
105

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4142
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4037
97.5%
1 105
 
2.5%

Length

2025-06-21T05:28:44.050450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-21T05:28:44.110864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4037
97.5%
1 105
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 4037
97.5%
1 105
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4037
97.5%
1 105
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4037
97.5%
1 105
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4037
97.5%
1 105
 
2.5%

BUMO PRE
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
0
2312 
1
1830 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4142
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2312
55.8%
1 1830
44.2%

Length

2025-06-21T05:28:44.185682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-21T05:28:44.247151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2312
55.8%
1 1830
44.2%

Most occurring characters

ValueCountFrequency (%)
0 2312
55.8%
1 1830
44.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2312
55.8%
1 1830
44.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2312
55.8%
1 1830
44.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2312
55.8%
1 1830
44.2%

Most Favourite
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
0
2548 
1
1594 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4142
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2548
61.5%
1 1594
38.5%

Length

2025-06-21T05:28:44.875384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-21T05:28:44.934808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2548
61.5%
1 1594
38.5%

Most occurring characters

ValueCountFrequency (%)
0 2548
61.5%
1 1594
38.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2548
61.5%
1 1594
38.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2548
61.5%
1 1594
38.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2548
61.5%
1 1594
38.5%

11 AM - before 2 PM
Real number (ℝ)

Zeros 

Distinct22
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.71511347
Minimum0
Maximum30
Zeros3451
Zeros (%)83.3%
Negative0
Negative (%)0.0%
Memory size64.7 KiB
2025-06-21T05:28:45.019923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum30
Range30
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.3307647
Coefficient of variation (CV)3.2592935
Kurtosis44.331383
Mean0.71511347
Median Absolute Deviation (MAD)0
Skewness5.6747961
Sum2962
Variance5.4324641
MonotonicityNot monotonic
2025-06-21T05:28:45.130689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 3451
83.3%
2 176
 
4.2%
3 109
 
2.6%
1 109
 
2.6%
4 107
 
2.6%
5 53
 
1.3%
6 28
 
0.7%
10 26
 
0.6%
8 19
 
0.5%
7 14
 
0.3%
Other values (12) 50
 
1.2%
ValueCountFrequency (%)
0 3451
83.3%
1 109
 
2.6%
2 176
 
4.2%
3 109
 
2.6%
4 107
 
2.6%
5 53
 
1.3%
6 28
 
0.7%
7 14
 
0.3%
8 19
 
0.5%
9 4
 
0.1%
ValueCountFrequency (%)
30 4
 
0.1%
22 1
 
< 0.1%
20 8
0.2%
18 1
 
< 0.1%
17 2
 
< 0.1%
16 3
 
0.1%
15 13
0.3%
14 1
 
< 0.1%
13 1
 
< 0.1%
12 11
0.3%

2 PM - before 5 PM
Real number (ℝ)

Zeros 

Distinct21
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.77667793
Minimum0
Maximum30
Zeros3306
Zeros (%)79.8%
Negative0
Negative (%)0.0%
Memory size64.7 KiB
2025-06-21T05:28:45.236150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum30
Range30
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.2675662
Coefficient of variation (CV)2.9195707
Kurtosis51.373915
Mean0.77667793
Median Absolute Deviation (MAD)0
Skewness5.8467487
Sum3217
Variance5.1418563
MonotonicityNot monotonic
2025-06-21T05:28:45.335864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 3306
79.8%
2 241
 
5.8%
3 142
 
3.4%
4 138
 
3.3%
1 127
 
3.1%
5 59
 
1.4%
10 34
 
0.8%
6 28
 
0.7%
8 23
 
0.6%
7 12
 
0.3%
Other values (11) 32
 
0.8%
ValueCountFrequency (%)
0 3306
79.8%
1 127
 
3.1%
2 241
 
5.8%
3 142
 
3.4%
4 138
 
3.3%
5 59
 
1.4%
6 28
 
0.7%
7 12
 
0.3%
8 23
 
0.6%
9 4
 
0.1%
ValueCountFrequency (%)
30 4
0.1%
26 1
 
< 0.1%
25 3
0.1%
24 1
 
< 0.1%
20 3
0.1%
16 2
 
< 0.1%
15 6
0.1%
13 3
0.1%
12 2
 
< 0.1%
11 3
0.1%

5 PM - before 9 PM
Real number (ℝ)

Zeros 

Distinct28
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9396427
Minimum0
Maximum30
Zeros1358
Zeros (%)32.8%
Negative0
Negative (%)0.0%
Memory size64.7 KiB
2025-06-21T05:28:45.456077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile10
Maximum30
Range30
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.6433261
Coefficient of variation (CV)1.2393772
Kurtosis13.067091
Mean2.9396427
Median Absolute Deviation (MAD)2
Skewness2.8261581
Sum12176
Variance13.273825
MonotonicityNot monotonic
2025-06-21T05:28:45.578527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 1358
32.8%
4 614
14.8%
2 578
14.0%
3 468
 
11.3%
1 293
 
7.1%
5 244
 
5.9%
6 162
 
3.9%
10 102
 
2.5%
8 100
 
2.4%
7 76
 
1.8%
Other values (18) 147
 
3.5%
ValueCountFrequency (%)
0 1358
32.8%
1 293
 
7.1%
2 578
14.0%
3 468
 
11.3%
4 614
14.8%
5 244
 
5.9%
6 162
 
3.9%
7 76
 
1.8%
8 100
 
2.4%
9 25
 
0.6%
ValueCountFrequency (%)
30 11
0.3%
26 3
 
0.1%
25 1
 
< 0.1%
24 3
 
0.1%
23 1
 
< 0.1%
22 3
 
0.1%
21 2
 
< 0.1%
20 17
0.4%
19 3
 
0.1%
18 3
 
0.1%

9 AM - before 11 AM
Real number (ℝ)

Zeros 

Distinct23
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.97561564
Minimum0
Maximum30
Zeros3206
Zeros (%)77.4%
Negative0
Negative (%)0.0%
Memory size64.7 KiB
2025-06-21T05:28:45.690165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum30
Range30
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.6708935
Coefficient of variation (CV)2.7376494
Kurtosis41.234719
Mean0.97561564
Median Absolute Deviation (MAD)0
Skewness5.3573907
Sum4041
Variance7.1336723
MonotonicityNot monotonic
2025-06-21T05:28:45.813261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 3206
77.4%
2 210
 
5.1%
4 182
 
4.4%
3 161
 
3.9%
1 120
 
2.9%
5 84
 
2.0%
6 45
 
1.1%
8 32
 
0.8%
10 26
 
0.6%
7 26
 
0.6%
Other values (13) 50
 
1.2%
ValueCountFrequency (%)
0 3206
77.4%
1 120
 
2.9%
2 210
 
5.1%
3 161
 
3.9%
4 182
 
4.4%
5 84
 
2.0%
6 45
 
1.1%
7 26
 
0.6%
8 32
 
0.8%
9 5
 
0.1%
ValueCountFrequency (%)
30 8
0.2%
25 1
 
< 0.1%
24 3
 
0.1%
21 2
 
< 0.1%
20 7
0.2%
19 1
 
< 0.1%
18 2
 
< 0.1%
16 1
 
< 0.1%
15 9
0.2%
13 3
 
0.1%

9 PM or later
Real number (ℝ)

Zeros 

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35852245
Minimum0
Maximum26
Zeros3690
Zeros (%)89.1%
Negative0
Negative (%)0.0%
Memory size64.7 KiB
2025-06-21T05:28:45.914780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4416607
Coefficient of variation (CV)4.0211168
Kurtosis87.624683
Mean0.35852245
Median Absolute Deviation (MAD)0
Skewness7.6285033
Sum1485
Variance2.0783855
MonotonicityNot monotonic
2025-06-21T05:28:46.033345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 3690
89.1%
2 160
 
3.9%
1 92
 
2.2%
4 64
 
1.5%
3 60
 
1.4%
5 23
 
0.6%
6 16
 
0.4%
10 13
 
0.3%
7 5
 
0.1%
15 4
 
0.1%
Other values (9) 15
 
0.4%
ValueCountFrequency (%)
0 3690
89.1%
1 92
 
2.2%
2 160
 
3.9%
3 60
 
1.4%
4 64
 
1.5%
5 23
 
0.6%
6 16
 
0.4%
7 5
 
0.1%
8 2
 
< 0.1%
9 4
 
0.1%
ValueCountFrequency (%)
26 1
 
< 0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
15 4
 
0.1%
14 1
 
< 0.1%
13 1
 
< 0.1%
12 3
 
0.1%
11 1
 
< 0.1%
10 13
0.3%
9 4
 
0.1%

Before 9 AM
Real number (ℝ)

Zeros 

Distinct29
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1197489
Minimum0
Maximum30
Zeros2741
Zeros (%)66.2%
Negative0
Negative (%)0.0%
Memory size64.7 KiB
2025-06-21T05:28:46.139402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile20.95
Maximum30
Range30
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.6894873
Coefficient of variation (CV)2.144239
Kurtosis6.5928717
Mean3.1197489
Median Absolute Deviation (MAD)0
Skewness2.6761208
Sum12922
Variance44.74924
MonotonicityNot monotonic
2025-06-21T05:28:46.250394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 2741
66.2%
4 233
 
5.6%
2 196
 
4.7%
3 121
 
2.9%
10 102
 
2.5%
5 93
 
2.2%
1 93
 
2.2%
30 90
 
2.2%
8 82
 
2.0%
6 64
 
1.5%
Other values (19) 327
 
7.9%
ValueCountFrequency (%)
0 2741
66.2%
1 93
 
2.2%
2 196
 
4.7%
3 121
 
2.9%
4 233
 
5.6%
5 93
 
2.2%
6 64
 
1.5%
7 27
 
0.7%
8 82
 
2.0%
9 11
 
0.3%
ValueCountFrequency (%)
30 90
2.2%
29 4
 
0.1%
28 5
 
0.1%
26 17
 
0.4%
25 21
 
0.5%
24 50
1.2%
23 4
 
0.1%
22 11
 
0.3%
21 6
 
0.1%
20 54
1.3%

DayPart_Unknown
Categorical

Imbalance 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
0.0
4139 
4.0
 
2
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12426
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 4139
99.9%
4.0 2
 
< 0.1%
1.0 1
 
< 0.1%

Length

2025-06-21T05:28:46.355520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-21T05:28:46.428724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4139
99.9%
4.0 2
 
< 0.1%
1.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 8281
66.6%
. 4142
33.3%
4 2
 
< 0.1%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12426
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 8281
66.6%
. 4142
33.3%
4 2
 
< 0.1%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12426
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 8281
66.6%
. 4142
33.3%
4 2
 
< 0.1%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12426
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 8281
66.6%
. 4142
33.3%
4 2
 
< 0.1%
1 1
 
< 0.1%

DayofWeek_Unknown
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
0
4125 
1
 
17

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4142
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4125
99.6%
1 17
 
0.4%

Length

2025-06-21T05:28:46.509478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-21T05:28:46.568060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4125
99.6%
1 17
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 4125
99.6%
1 17
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4125
99.6%
1 17
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4125
99.6%
1 17
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4125
99.6%
1 17
 
0.4%

Weekdays
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
1
2566 
0
1576 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4142
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 2566
62.0%
0 1576
38.0%

Length

2025-06-21T05:28:46.640938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-21T05:28:46.707975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 2566
62.0%
0 1576
38.0%

Most occurring characters

ValueCountFrequency (%)
1 2566
62.0%
0 1576
38.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2566
62.0%
0 1576
38.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2566
62.0%
0 1576
38.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2566
62.0%
0 1576
38.0%

Weekends
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
1
3879 
0
 
263

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4142
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 3879
93.7%
0 263
 
6.3%

Length

2025-06-21T05:28:46.782927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-21T05:28:46.842795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 3879
93.7%
0 263
 
6.3%

Most occurring characters

ValueCountFrequency (%)
1 3879
93.7%
0 263
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3879
93.7%
0 263
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3879
93.7%
0 263
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3879
93.7%
0 263
 
6.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
1.0
3624 
0.0
518 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12426
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 3624
87.5%
0.0 518
 
12.5%

Length

2025-06-21T05:28:46.917193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-21T05:28:46.980882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3624
87.5%
0.0 518
 
12.5%

Most occurring characters

ValueCountFrequency (%)
0 4660
37.5%
. 4142
33.3%
1 3624
29.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12426
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4660
37.5%
. 4142
33.3%
1 3624
29.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12426
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4660
37.5%
. 4142
33.3%
1 3624
29.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12426
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4660
37.5%
. 4142
33.3%
1 3624
29.2%

Socializing
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
1.0
3909 
0.0
 
233

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12426
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 3909
94.4%
0.0 233
 
5.6%

Length

2025-06-21T05:28:47.075139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-21T05:28:47.135179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3909
94.4%
0.0 233
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 4375
35.2%
. 4142
33.3%
1 3909
31.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12426
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4375
35.2%
. 4142
33.3%
1 3909
31.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12426
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4375
35.2%
. 4142
33.3%
1 3909
31.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12426
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4375
35.2%
. 4142
33.3%
1 3909
31.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
0.0
2596 
1.0
1546 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12426
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2596
62.7%
1.0 1546
37.3%

Length

2025-06-21T05:28:47.212470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-21T05:28:47.274520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2596
62.7%
1.0 1546
37.3%

Most occurring characters

ValueCountFrequency (%)
0 6738
54.2%
. 4142
33.3%
1 1546
 
12.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12426
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6738
54.2%
. 4142
33.3%
1 1546
 
12.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12426
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6738
54.2%
. 4142
33.3%
1 1546
 
12.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12426
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6738
54.2%
. 4142
33.3%
1 1546
 
12.4%

Meals & Snack
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
0.0
3553 
1.0
589 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12426
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3553
85.8%
1.0 589
 
14.2%

Length

2025-06-21T05:28:47.353209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-21T05:28:47.415681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3553
85.8%
1.0 589
 
14.2%

Most occurring characters

ValueCountFrequency (%)
0 7695
61.9%
. 4142
33.3%
1 589
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12426
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7695
61.9%
. 4142
33.3%
1 589
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12426
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7695
61.9%
. 4142
33.3%
1 589
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12426
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7695
61.9%
. 4142
33.3%
1 589
 
4.7%

Studying
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
0.0
3994 
1.0
 
148

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12426
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3994
96.4%
1.0 148
 
3.6%

Length

2025-06-21T05:28:47.494052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-21T05:28:47.556272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3994
96.4%
1.0 148
 
3.6%

Most occurring characters

ValueCountFrequency (%)
0 8136
65.5%
. 4142
33.3%
1 148
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12426
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 8136
65.5%
. 4142
33.3%
1 148
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12426
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 8136
65.5%
. 4142
33.3%
1 148
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12426
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 8136
65.5%
. 4142
33.3%
1 148
 
1.2%

NeedState_Other
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
0.0
4125 
1.0
 
17

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12426
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 4125
99.6%
1.0 17
 
0.4%

Length

2025-06-21T05:28:47.631534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-21T05:28:47.698265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4125
99.6%
1.0 17
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 8267
66.5%
. 4142
33.3%
1 17
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12426
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 8267
66.5%
. 4142
33.3%
1 17
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12426
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 8267
66.5%
. 4142
33.3%
1 17
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12426
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 8267
66.5%
. 4142
33.3%
1 17
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
0.0
3615 
1.0
527 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12426
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3615
87.3%
1.0 527
 
12.7%

Length

2025-06-21T05:28:47.771256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-21T05:28:47.831422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3615
87.3%
1.0 527
 
12.7%

Most occurring characters

ValueCountFrequency (%)
0 7757
62.4%
. 4142
33.3%
1 527
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12426
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7757
62.4%
. 4142
33.3%
1 527
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12426
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7757
62.4%
. 4142
33.3%
1 527
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12426
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7757
62.4%
. 4142
33.3%
1 527
 
4.2%

PPA
Real number (ℝ)

High correlation  Zeros 

Distinct49
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.478593
Minimum0
Maximum200
Zeros1365
Zeros (%)33.0%
Negative0
Negative (%)0.0%
Memory size64.7 KiB
2025-06-21T05:28:47.941884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median40
Q350
95-th percentile70
Maximum200
Range200
Interquartile range (IQR)50

Descriptive statistics

Standard deviation27.348011
Coefficient of variation (CV)0.79318814
Kurtosis-0.41559943
Mean34.478593
Median Absolute Deviation (MAD)20
Skewness0.1054118
Sum142810.33
Variance747.91372
MonotonicityNot monotonic
2025-06-21T05:28:48.102808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 1365
33.0%
50 729
17.6%
60 333
 
8.0%
40 290
 
7.0%
49 257
 
6.2%
39 154
 
3.7%
30 117
 
2.8%
70 112
 
2.7%
29 111
 
2.7%
35 100
 
2.4%
Other values (39) 574
13.9%
ValueCountFrequency (%)
0 1365
33.0%
12 1
 
< 0.1%
14 1
 
< 0.1%
15 3
 
0.1%
20 8
 
0.2%
25 7
 
0.2%
28 1
 
< 0.1%
29 111
 
2.7%
30 117
 
2.8%
32 1
 
< 0.1%
ValueCountFrequency (%)
200 1
 
< 0.1%
160 1
 
< 0.1%
125 1
 
< 0.1%
120 8
 
0.2%
110 2
 
< 0.1%
100 85
2.1%
95 1
 
< 0.1%
90 4
 
0.1%
85 4
 
0.1%
80 75
1.8%

Churn
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
0
2690 
1
1452 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4142
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 2690
64.9%
1 1452
35.1%

Length

2025-06-21T05:28:48.228553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-21T05:28:48.293811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2690
64.9%
1 1452
35.1%

Most occurring characters

ValueCountFrequency (%)
0 2690
64.9%
1 1452
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2690
64.9%
1 1452
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2690
64.9%
1 1452
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4142
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2690
64.9%
1 1452
35.1%

Interactions

2025-06-21T05:28:39.097850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:21.989914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:23.391004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:24.859905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:27.059822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:28.333941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:29.632645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:31.315070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:32.653603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:33.980165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:35.322203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:37.178761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:39.275960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:22.109146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:23.495918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:25.423617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:27.173661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:28.446755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:29.748287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:31.442844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:32.769753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:34.093045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:35.434337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:37.354400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:39.444669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:22.214721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:23.600448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:25.565414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:27.283436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:28.550579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:29.850060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:31.548903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:32.874428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:34.211791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:35.547551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:37.508249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:39.579298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:22.337797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:23.708983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:25.704800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:27.386321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:28.646421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:29.954280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:31.652145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:32.984284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:34.326957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:35.650249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:37.674641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:39.688655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:22.444941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:23.805023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:25.847100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:27.484172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:28.748592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:30.059191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:31.755515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:33.087063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:34.432704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:35.752537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:37.823961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:39.811401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:22.557481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:23.902201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:25.993875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:27.585231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:28.852518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:30.170947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:31.861257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:33.199838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:34.556388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:35.865870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:37.978988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:39.932558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:22.671632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:24.004823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:26.170119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:27.689059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:28.962467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:30.282766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:31.970545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:33.309470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:34.659272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:35.973728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:38.140328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:40.045631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:22.787687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:24.125614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:26.343413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:27.788438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:29.064919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:30.774378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:32.093753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:33.427049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:34.767101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:36.081214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:38.292743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:40.158698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:22.910190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:24.236022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:26.515898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:27.903495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:29.179286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:30.880188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:32.202932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:33.552829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:34.872427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:36.191359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:38.448895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:40.277443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:23.037187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:24.388826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:26.670327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:28.008772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:29.299447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:30.986590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:32.316871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:33.662044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:34.982236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:36.291235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:38.595210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:40.389129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:23.146575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:24.545202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:26.829281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:28.106986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:29.413326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:31.088526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:32.428806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:33.761249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:35.093956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:36.858945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:38.759503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:40.501155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:23.272339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:24.702059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:26.954362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:28.204486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:29.522225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:31.196478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:32.540198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:33.868598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:35.200791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:37.013198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-21T05:28:38.919324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-21T05:28:48.409791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
11 AM - before 2 PM2 PM - before 5 PM5 PM - before 9 PM9 AM - before 11 AM9 PM or laterAgeBUMOBUMO PREBefore 9 AMBrand LikabilityChurnDailyDayPart_UnknownDayofWeek_UnknownDrinking beveragesGenderMPIMeals & SnackMost FavouriteNPS#P3MNPS#P3M#GroupNeedState_OtherOccupation#groupPPARelaxing & entertainmentSocializingSpendStudyingTOMVisitWeekdaysWeekendsWeeklyWorking & business meeting
11 AM - before 2 PM1.000-0.013-0.015-0.0790.0120.0230.0000.059-0.0050.0820.0440.0830.0000.0580.0000.0940.0630.0710.0380.0290.0270.0000.028-0.0220.0640.0120.0120.0550.0290.0090.1610.1210.0480.109
2 PM - before 5 PM-0.0131.000-0.1230.110-0.014-0.0230.0000.011-0.0700.0390.0000.0470.0000.0000.0000.055-0.0030.0370.0280.0210.0820.1160.051-0.0160.0000.0450.0160.0730.0390.0240.1350.0180.0440.066
5 PM - before 9 PM-0.015-0.1231.000-0.0330.060-0.0920.0290.030-0.1170.0870.1070.1060.0000.0000.0000.0720.0520.0680.0350.0500.0640.0000.0520.1400.1710.0640.2060.0520.0590.2020.1910.0810.1820.048
9 AM - before 11 AM-0.0790.110-0.0331.000-0.0460.0490.0560.000-0.2080.0460.0000.0500.0000.0000.0320.0470.1170.0120.000-0.0050.0260.0000.050-0.0280.0460.0000.0540.0060.0000.0690.1050.0000.0860.106
9 PM or later0.012-0.0140.060-0.0461.000-0.0580.0370.000-0.0930.0000.0340.0490.0000.1000.0530.0680.0720.0020.0350.0540.0150.0000.0680.0910.1130.0320.0690.0440.0000.0570.0780.0250.0700.070
Age0.023-0.023-0.0920.049-0.0581.0000.0280.1130.2320.0220.0930.0410.0000.0500.0520.0290.4310.0090.133-0.0820.0620.0230.276-0.0960.0930.048-0.0700.2430.110-0.0630.1040.0000.0770.125
BUMO0.0000.0000.0290.0560.0370.0281.0000.0000.0000.0000.0310.0200.0000.0000.0000.0140.0000.0130.0000.0000.0000.0000.0240.0240.0320.0000.0000.0170.1110.0000.0000.0000.0440.000
BUMO PRE0.0590.0110.0300.0000.0000.1130.0001.0000.1070.0000.5210.1950.0210.0000.0000.0750.0260.0670.6930.4660.4630.0000.0630.5000.0280.0000.4420.0000.7830.4020.0720.0000.4860.000
Before 9 AM-0.005-0.070-0.117-0.208-0.0930.2320.0000.1071.0000.0940.0870.0670.0490.1110.0380.1930.1290.0000.155-0.1510.1010.0300.084-0.1680.1060.048-0.0580.0000.199-0.0300.2710.0450.0400.113
Brand Likability0.0820.0390.0870.0460.0000.0220.0000.0000.0941.0000.0000.6850.0000.0000.1150.0050.2340.0280.0000.0750.0740.0000.1110.0570.0000.0340.0290.0000.0380.0510.0000.0280.3040.013
Churn0.0440.0000.1070.0000.0340.0930.0310.5210.0870.0001.0000.3670.0000.0120.0000.0280.0200.0480.4890.3450.3380.0000.0940.9460.0100.0000.3570.0340.5460.3290.0000.0320.6030.000
Daily0.0830.0470.1060.0500.0490.0410.0200.1950.0670.6850.3671.0000.0000.0000.1020.0080.1890.0000.2150.1540.1550.0090.0740.3550.0470.0000.2300.0000.2550.2770.0490.0000.6100.017
DayPart_Unknown0.0000.0000.0000.0000.0000.0000.0000.0210.0490.0000.0000.0001.0000.2410.0430.0050.0000.0000.0260.0000.0000.0000.0000.0000.0140.0600.0200.0000.0290.0230.0210.0560.0000.000
DayofWeek_Unknown0.0580.0000.0000.0000.1000.0500.0000.0000.1110.0000.0120.0000.2411.0000.0350.0220.0620.0000.0000.0000.0000.0000.0600.0000.0280.0200.0780.0090.0110.1500.0030.0340.0490.000
Drinking beverages0.0000.0000.0000.0320.0530.0520.0000.0000.0380.1150.0000.1020.0430.0351.0000.0460.0140.0610.0160.0410.0290.0000.0670.0480.0320.0140.0000.0270.0000.0400.0790.0000.0420.028
Gender0.0940.0550.0720.0470.0680.0290.0140.0750.1930.0050.0280.0080.0050.0220.0461.0000.0930.1270.0740.0640.0680.0000.1600.1060.0810.0000.0240.0160.0850.0470.1670.0210.0000.134
MPI0.063-0.0030.0520.1170.0720.4310.0000.0260.1290.2340.0200.1890.0000.0620.0140.0931.0000.0000.0000.0330.0260.1670.187-0.0040.0350.0000.0540.0300.0000.0610.0690.0000.1070.085
Meals & Snack0.0710.0370.0680.0120.0020.0090.0130.0670.0000.0280.0480.0000.0000.0000.0610.1270.0001.0000.0600.0560.0600.0140.0790.0890.0410.0370.1020.0230.0780.1010.0260.0390.0590.026
Most Favourite0.0380.0280.0350.0000.0350.1330.0000.6930.1550.0000.4890.2150.0260.0000.0160.0740.0000.0601.0000.4110.4090.0000.0710.4740.0000.0000.4430.0000.7500.4020.0650.0000.4840.000
NPS#P3M0.0290.0210.050-0.0050.054-0.0820.0000.466-0.1510.0750.3450.1540.0000.0000.0410.0640.0330.0560.4111.0000.9990.0690.0350.3650.0820.0690.4410.0350.4550.4310.0690.0000.3060.029
NPS#P3M#Group0.0270.0820.0640.0260.0150.0620.0000.4630.1010.0740.3380.1550.0000.0000.0290.0680.0260.0600.4090.9991.0000.0460.0570.2020.0600.0000.1490.0000.4540.1330.0740.0000.3040.036
NeedState_Other0.0000.1160.0000.0000.0000.0230.0000.0000.0300.0000.0000.0090.0000.0000.0000.0000.1670.0140.0000.0690.0461.0000.0450.0000.0000.0730.0000.0000.0000.0000.0000.0160.0000.000
Occupation#group0.0280.0510.0520.0500.0680.2760.0240.0630.0840.1110.0940.0740.0000.0600.0670.1600.1870.0790.0710.0350.0570.0451.0000.0480.0410.0510.0290.2410.0850.0550.1350.0090.0650.194
PPA-0.022-0.0160.140-0.0280.091-0.0960.0240.500-0.1680.0570.9460.3550.0000.0000.0480.106-0.0040.0890.4740.3650.2020.0000.0481.0000.0270.0670.7930.0230.5330.7020.0200.0670.5700.063
Relaxing & entertainment0.0640.0000.1710.0460.1130.0930.0320.0280.1060.0000.0100.0470.0140.0280.0320.0810.0350.0410.0000.0820.0600.0000.0410.0271.0000.0390.0670.0880.0300.0630.0810.0500.0380.045
Socializing0.0120.0450.0640.0000.0320.0480.0000.0000.0480.0340.0000.0000.0600.0200.0140.0000.0000.0370.0000.0690.0000.0730.0510.0670.0391.0000.0480.0370.0000.0140.0000.0570.0000.000
Spend0.0120.0160.2060.0540.069-0.0700.0000.442-0.0580.0290.3570.2300.0200.0780.0000.0240.0540.1020.4430.4410.1490.0000.0290.7930.0670.0481.0000.0270.5240.9820.1530.0000.5990.141
Studying0.0550.0730.0520.0060.0440.2430.0170.0000.0000.0000.0340.0000.0000.0090.0270.0160.0300.0230.0000.0350.0000.0000.2410.0230.0880.0370.0271.0000.0000.0220.0000.0290.0000.000
TOM0.0290.0390.0590.0000.0000.1100.1110.7830.1990.0380.5460.2550.0290.0110.0000.0850.0000.0780.7500.4550.4540.0000.0850.5330.0300.0000.5240.0001.0000.4690.0910.0120.5730.000
Visit0.0090.0240.2020.0690.057-0.0630.0000.402-0.0300.0510.3290.2770.0230.1500.0400.0470.0610.1010.4020.4310.1330.0000.0550.7020.0630.0140.9820.0220.4691.0000.1840.0000.5740.147
Weekdays0.1610.1350.1910.1050.0780.1040.0000.0720.2710.0000.0000.0490.0210.0030.0790.1670.0690.0260.0650.0690.0740.0000.1350.0200.0810.0000.1530.0000.0910.1841.0000.2020.0810.136
Weekends0.1210.0180.0810.0000.0250.0000.0000.0000.0450.0280.0320.0000.0560.0340.0000.0210.0000.0390.0000.0000.0000.0160.0090.0670.0500.0570.0000.0290.0120.0000.2021.0000.0000.042
Weekly0.0480.0440.1820.0860.0700.0770.0440.4860.0400.3040.6030.6100.0000.0490.0420.0000.1070.0590.4840.3060.3040.0000.0650.5700.0380.0000.5990.0000.5730.5740.0810.0001.0000.062
Working & business meeting0.1090.0660.0480.1060.0700.1250.0000.0000.1130.0130.0000.0170.0000.0000.0280.1340.0850.0260.0000.0290.0360.0000.1940.0630.0450.0000.1410.0000.0000.1470.1360.0420.0621.000

Missing values

2025-06-21T05:28:40.764666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-21T05:28:41.080603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IsAwarenessTrialBrand LikabilityWeeklyDailyComprehensionVisitSpendSegmentationFullNPS#P3MNPS#P3M#GroupAgeMPIGenderOccupation#groupTOMBUMOBUMO PREMost Favourite11 AM - before 2 PM2 PM - before 5 PM5 PM - before 9 PM9 AM - before 11 AM9 PM or laterBefore 9 AMDayPart_UnknownDayofWeek_UnknownWeekdaysWeekendsDrinking beveragesSocializingRelaxing & entertainmentMeals & SnackStudyingNeedState_OtherWorking & business meetingPPAChurn
436811100Know a little00.008Passive388249MaleBlue Collar00000.02.00.00.00.02.00.00011.01.00.00.00.00.00.00.01
436911100Know a little00.008Passive183749FemaleNone Working00000.02.06.00.00.00.00.00011.01.00.00.00.00.00.00.01
437011100Know a little00.008Passive4437499MaleSelf Employed - Company Owner00000.00.00.02.00.00.00.00011.01.00.00.00.00.00.00.01
437111100Know a little00.008Passive3010499MaleBlue Collar00000.00.00.05.00.00.00.00101.01.00.00.00.00.00.00.01
437211100Know a little00.008Passive396999MaleWhite Collar00000.00.04.00.00.00.00.00011.01.00.00.00.00.00.00.01
437311100Know a little00.008Passive5410499FemaleSelf Employed - Small Business and Freelance00000.00.02.00.00.00.00.00011.01.00.00.00.00.00.00.01
437411100Know a little00.008Passive3810499MaleBlue Collar00000.00.00.05.00.00.00.00111.01.00.00.00.00.00.00.01
437511100Know a little00.008Passive175499FemaleNone Working00000.00.04.02.00.00.00.00011.01.00.01.00.00.00.00.01
437611100Know a little00.008Passive363749FemaleBlue Collar00000.00.00.00.00.012.00.00111.01.01.00.00.00.00.00.01
437711100Know a little00.008Passive213749FemaleNone Working00000.00.03.02.00.00.00.00011.01.00.01.00.00.00.00.01
IsAwarenessTrialBrand LikabilityWeeklyDailyComprehensionVisitSpendSegmentationFullNPS#P3MNPS#P3M#GroupAgeMPIGenderOccupation#groupTOMBUMOBUMO PREMost Favourite11 AM - before 2 PM2 PM - before 5 PM5 PM - before 9 PM9 AM - before 11 AM9 PM or laterBefore 9 AMDayPart_UnknownDayofWeek_UnknownWeekdaysWeekendsDrinking beveragesSocializingRelaxing & entertainmentMeals & SnackStudyingNeedState_OtherWorking & business meetingPPAChurn
8510110000129.0Seg.02 - Mass Asp (VND 25K - VND 59K)8Passive265499MaleWhite Collar00000.00.00.00.00.024.00.00111.01.00.01.00.00.00.029.00
8511110000140.0Seg.02 - Mass Asp (VND 25K - VND 59K)8Passive171499FemaleNone Working01100.00.04.00.00.00.00.00011.00.01.00.00.00.00.040.00
8512110000150.0Seg.02 - Mass Asp (VND 25K - VND 59K)8Passive336999MaleWhite Collar00000.00.00.04.00.00.00.00111.01.00.00.00.00.01.050.00
8513110000149.0Seg.02 - Mass Asp (VND 25K - VND 59K)8Passive255499FemaleWhite Collar00000.00.05.00.00.03.00.00111.01.00.00.00.00.00.049.00
8514110000139.0Seg.02 - Mass Asp (VND 25K - VND 59K)8Passive355499FemaleBlue Collar00000.00.00.00.00.00.00.00010.01.00.00.00.00.00.039.00
8515110000149.0Seg.02 - Mass Asp (VND 25K - VND 59K)8Passive243749FemaleNone Working00000.00.08.00.00.00.00.00111.00.00.00.00.00.00.049.00
8516110000149.0Seg.02 - Mass Asp (VND 25K - VND 59K)8Passive386999FemaleWhite Collar00000.00.03.00.00.00.00.00011.01.00.00.00.00.00.049.00
8517110000139.0Seg.02 - Mass Asp (VND 25K - VND 59K)8Passive233749MaleNone Working00000.00.01.02.00.00.00.00111.01.00.00.01.00.00.039.00
8518110000149.0Seg.02 - Mass Asp (VND 25K - VND 59K)8Passive205499FemaleBlue Collar00000.01.00.00.00.00.00.00011.01.00.00.00.00.00.049.00
8519110000149.0Seg.02 - Mass Asp (VND 25K - VND 59K)8Passive256999MaleSelf Employed - Small Business and Freelance00100.00.02.00.00.01.00.00010.01.00.00.00.00.00.049.00